Robust vegetation segmentation under field conditions using new adaptive weights for hybrid multichannel images based on the Chan-Vese model

This paper proposes a method for detecting vegetation in agricultural images under real field conditions. It in-cludes two modules: The first module constructs a multichannel image by combining four color indices and the L*a*b* color space using Principal Component Analysis (PCA). The second module detects the vegetation by applying an improved Chan-Vese method. In this method, the energy weights are automatically estimated based on the contrast between foreground regions and the background. To speed up the segmentation process a sweeping algorithm is applied. Experimental results demonstrate that our algorithm outperforms ten state-of-the-art methods, yielding higher accuracy, precision, and achieving better recall and F-score rates. The main advantage of the proposed method is that it performs well under different field conditions. On the seven datasets considered in this work, the proposed method achieved 97.10%, 95.70%, 95.70%, and 96.37% averages in terms of accuracy, F-score, precision, and recall respectively.